Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience.
The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work.
1. Chapter 1 Introduction of artificial intelligence in Earth sciences - p. 1 2. Chapter 2 Machine learning for snow cover mapping - p. 16 3. Chapter 3 AI for sea ice forecasting - p. 40 4. Chapter 4 Deep learning for ocean mesoscale eddy detection - p. 59 5. Chapter 5 Artificial intelligence for plant disease recognition - p. 100 6. Chapter 6 Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread - p. 119 7. Chapter 7 AI for physics-inspired hydrology modeling - p. 157 8. Chapter 8 Theory of spatiotemporal deep analogs and their application to solar forecasting - p. 204 9. Chapter 9 AI for improving ozone forecasting - p. 247 10. Chapter 10 AI for monitoring power plant emissions from space - p. 270 11. Chapter 11 AI for shrubland identification and mapping - p. 295 12. Chapter 12 Explainable AI for understanding ML-derived vegetation products - p. 317 13. Chapter 13 Satellite image classification using quantum machine learning - p. 336 14. Chapter 14 Provenance in earth AI - p. 356 15. Chapter 15 AI ethics for earth sciences - p. 379 16. Index - p. 397 17. Index - Continued - p. 411
Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience.
The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work.
1. Chapter 1 Introduction of artificial intelligence in Earth sciences - p. 1 2. Chapter 2 Machine learning for snow cover mapping - p. 16 3. Chapter 3 AI for sea ice forecasting - p. 40 4. Chapter 4 Deep learning for ocean mesoscale eddy detection - p. 59 5. Chapter 5 Artificial intelligence for plant disease recognition - p. 100 6. Chapter 6 Spatiotemporal attention ConvLSTM networks for predicting and physically interpreting wildfire spread - p. 119 7. Chapter 7 AI for physics-inspired hydrology modeling - p. 157 8. Chapter 8 Theory of spatiotemporal deep analogs and their application to solar forecasting - p. 204 9. Chapter 9 AI for improving ozone forecasting - p. 247 10. Chapter 10 AI for monitoring power plant emissions from space - p. 270 11. Chapter 11 AI for shrubland identification and mapping - p. 295 12. Chapter 12 Explainable AI for understanding ML-derived vegetation products - p. 317 13. Chapter 13 Satellite image classification using quantum machine learning - p. 336 14. Chapter 14 Provenance in earth AI - p. 356 15. Chapter 15 AI ethics for earth sciences - p. 379 16. Index - p. 397 17. Index - Continued - p. 411
